Evaluating self-supervised echocardiographic representations across downstream extraction strategies for left-ventricular segmentation and ejection fraction estimation

• URL: http://arxiv.org/abs/2606.22943v1
• Date: Mon, 22 Jun 2026 07:21:43 GMT
• Title: Evaluating self-supervised echocardiographic representations across downstream extraction strategies for left-ventricular segmentation and ejection fraction estimation
• Abstract summary: Self-supervised learning (SSL) is increasingly used in medical imaging to reduce annotation requirements.<n>For dense clinical tasks, this can confound representation quality with the capacity of the downstream model used to recover task-relevant information.<n>We present a systematic evaluation of self-supervised representations for left-ventricular segmentation and ejection fraction estimation.
• Score: 2.0650491087688496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) is increasingly used in medical imaging to reduce annotation requirements, but representation quality is often judged using a single downstream evaluation setting. For dense clinical tasks, this can confound representation quality with the capacity of the downstream model used to recover task-relevant information. We present a systematic evaluation of self-supervised representations for left-ventricular segmentation and ejection fraction (EF) estimation from apical four-chamber echocardiography on EchoNet-Dynamic. Rather than relying on a single downstream probe, we compare a hierarchy of extraction strategies with increasing expressivity: heuristic extraction without mask-supervised training, frozen linear probes, frozen lightweight decoder probes, and partial fine-tuning. We apply this framework to two complementary representation families: generic frozen self-DIstillation with NO labels (DINOv3) features and a task-adapted dense self-supervised representation, Bootstrap Your Own Segmentation (BYOS). In both families, heuristic extraction substantially understated what was recoverable from the frozen representation. For DINOv3, performance improved from Dice 0.684 and EF mean absolute error (MAE) 13.01 under heuristic extraction to Dice 0.906 and EF MAE 9.65 with a frozen lightweight decoder, approaching a supervised U-Net baseline (Dice 0.915, EF MAE 9.72). For BYOS, performance improved from Dice 0.687 and EF MAE 17.83 under heuristic extraction to Dice 0.902 and EF MAE 8.74 with a frozen lightweight decoder. These results show that conclusions about self-supervised representation quality in dense echocardiographic analysis depend strongly on the downstream extraction strategy used for evaluation. We therefore argue that multi-strategy evaluation is an important methodological consideration for SSL in dense medical image analysis.

Related papers

• Zero-Shot Respiratory Sound Classification through LLM-Augmented Audio-Text Alignment [29.08356862626618]
  Self-supervised respiratory encoders lack semantic grounding in clinical domain needed for zero-shot inference.<n>We propose a framework that aligns these encoders with medical terminology in a shared latent space turning them into a zero-shot-capable foundation model.
  arXiv  Detail & Related papers  (2026-08-30T11:49:54Z)
• Unsupervised Anatomical Feature Learning via Diffusion Models: Enhanced Medical Image Segmentation with Denoising Diffusion Probabilistic Models [0.8590539335179757]
  This research paper proposes utilizing unsupervised Denoising Diffusion Probabilistic Models (DDPMs) to extract anatomical features.<n>We train a DDPM on 21 unlabeled abdominal CT scans to learn structural representations, transferring the encoder weights to a downstream segmentation task.<n>Using unlabeled images for diffusion-based pretraining successfully embeds robust anatomical features prior to human supervision.
  arXiv  Detail & Related papers  (2026-08-26T12:10:56Z)
• EPRA U-Net: An Efficient Pyramid Residual Attention Framework for Accurate Infarct Segmentation in Diffusion-Weighted MRI [18.07144782374738]
  EPRA U-Net is a task-specific integrated architecture for efficient and accurate infarct segmentation of DWI images.<n>EPRA U-Net attained superior performance compared with state-of-the-art models.
  arXiv  Detail & Related papers  (2026-07-03T19:17:51Z)
• DINO-MVR: Multi-View Readout of Frozen DINOv3 for Annotation-Efficient Medical Segmentation [10.929173865297933]
  We show that frozen DINOv3 features already contain useful structural and boundary cues for medical segmentation.<n>We propose DINO-MVR, a Multi-View Readout framework for annotation-efficient medical segmentation.
  arXiv  Detail & Related papers  (2026-05-08T04:13:01Z)
• Anatomical Token Uncertainty for Transformer-Guided Active MRI Acquisition [27.825869252861335]
  Full data acquisition in MRI is inherently slow, which limits clinical throughput and increases patient discomfort.<n>CSMRI seeks to accelerate acquisition by reconstructing images from under-sampled kspace data.
  arXiv  Detail & Related papers  (2026-03-23T10:49:23Z)
• EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models [82.43729208063468]
  Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety.<n>We study sycophancy -- models' tendency to uncritically echo user-provided information.<n>We introduce EchoBench, a benchmark to systematically evaluate sycophancy in medical LVLMs.
  arXiv  Detail & Related papers  (2025-09-24T14:09:55Z)
• A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
  We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
  arXiv  Detail & Related papers  (2025-08-19T14:41:22Z)
• SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms [60.35639972035727]
  The lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. Dice scores reached up to 0.838 $pm$ 0.066 and 0.716 $pm$ 0.125 on the respective datasets, with an average performance of up to 0.804 $pm$ 0.15.
  arXiv  Detail & Related papers  (2024-11-14T17:06:00Z)
• Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI [11.871709357017416]
  We propose a feature reduction pipeline using resting-state fMRI data.<n>We used Craddock atlas and Power atlas to extract functional connectivity data from rs-fMRI.<n>By using a denoising variational autoencoder, our proposed pipeline further compresses the connectivity features into 5 latent Gaussian distributions.
  arXiv  Detail & Related papers  (2024-09-30T09:38:47Z)
• FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures [4.507779218329283]
  Segmenting retinal vessels presents unique challenges.<n>Recent neural network approaches struggle to balance local and global properties.<n>We propose a comprehensive micro-vessel extraction mechanism based on an encoder-decoder neural network architecture.
  arXiv  Detail & Related papers  (2023-11-14T10:32:17Z)
• Weakly supervised segmentation of intracranial aneurysms using a novel 3D focal modulation UNet [0.5106162890866905]
  We propose FocalSegNet, a novel 3D focal modulation UNet, to detect an aneurysm and offer an initial, coarse segmentation of it from time-of-flight MRA image patches. We trained and evaluated our model on a public dataset, and in terms of UIA detection, our model showed a low false-positive rate of 0.21 and a high sensitivity of 0.80.
  arXiv  Detail & Related papers  (2023-08-06T03:28:08Z)
• Skeleton Graph-based Ultrasound-CT Non-rigid Registration [40.40844749663152]
  We propose a skeleton graph-based non-rigid registration to adapt patient-specific properties using subcutaneous bone surface features. To validate the proposed approach, we manually extract the US cartilage point cloud from one volunteer and seven CT cartilage point clouds from different patients.
  arXiv  Detail & Related papers  (2023-05-14T19:21:43Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.